One of the biggest challenges for employers in the management of their health plan options is determining the employee premium contribution structure. Most employers are driven by cost control, and want to create an incentive for employees to enroll in efficient plans. However, pricing schemes typically ignore the fact that efficiency cannot be determined by a simple comparison of average per-person costs across plans, because of the influence of patient selection. For example, the impact of selection may cause a closed-panel HMO to appear disproportionately efficient, as healthy people migrate to this low-cost option. This research will explore the interrelationships between pricing, employee choice and the resulting average costs, in an effort to separate the selection impact from the influence of plan efficiency on average costs. Specifically, the applicant will: 1. Develop a utility-based choice model to predict the patient's choice of plan, given contribution structure; 2. Develop a cost model to explore how patterns of patient choice influence average cost by plan, allowing the influence of selection to be separated from other plan cost drivers; 3. Model the impact of different pricing philosophies in both self-insured and fully-insured scenarios. These models will be based on three years of enrollment data and two years of claims data from a large employer with four plan options. Detailed claims data allows the use of the Johns Hopkins Adjusted Clinical Groups (ACG) Case-Mix software to develop measures of the health status of each individual, to be used as a covariate in the modeling. Bayesian inference with Gibbs sampling will be used to estimate a multinomial probit choice model for health plan elections. The applicant will evaluate two hierarchical cost models that take into account the selection effect of seeking care, with random effects to capture the influence of correlation within families. Using these models as tools, the stability of the plan offerings will be explored under various employer pricing philosophies: percent of premium contribution, fixed dollar contribution, and fixed dollar contribution adjusted for the plans' health risk mix. In the fully-insured scenario, the problem extends to solving the insurers' Nash equilibrium problem, as they set their premium levels, given knowledge of the employer's contribution strategy.